Forehead Flap Templates for Nasal Reconstruction Digitally Developed From 2D and 3D Images
Bibliographic record
Abstract
ABSTRACT: The forehead flap is the gold standard procedure for nasal reconstruction to address a partial or complete rhinectomy. Traditionally, the three-dimensional (3D) nasal defect is manually templated intraoperatively to design the two-dimensional (2D) flap shape on intact morphology. In this clinical study, digital preoperative planning is used to template with computer-assisted design and manufacturing. Preoperative digital templates were implemented for 3 representative patients (1 in Supplementary Digital Content, http://links.lww.com/SCS/D60). This includes designs for a hemi-rhinectomy case from 3D mirroring, a partial total rhinectomy case generated from a 3D scan, and a total rhinectomy case generated from a 3D morphable model based on a prepathology 2D photo. Digital unwrapping flattened the patient's 3D nasal geometry designs to 2D skin flap shapes. Finally, the 2D designs were printed as traceable intraoperative templates at a 1:1 scale. This clinical study demonstrates the application of digital 3D preoperative templating to improve workflow for nasal reconstruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".